Add missing -lp shortcut for --launch-profile in dotnet run documentation

docs/core/dependency-loading/default-probing.md

@@ -27,10 +27,12 @@
 
 - When the *\*.deps.json* file is present, it's parsed to populate the probing properties.
 - When the *\*.deps.json* file isn't present, the application's directory is assumed to contain all the dependencies. The directory's contents are used to populate the probing properties.
-
 Additionally, the *\*.deps.json* files for any referenced frameworks are similarly parsed.
-
 The environment variable `DOTNET_ADDITIONAL_DEPS` can be used to add additional dependencies.  `dotnet.exe` also contains an optional `--additional-deps` parameter to set this value on application startup.
+> [!NOTE]
+> The `DOTNET_ADDITIONAL_DEPS` environment variable and the `--additional-deps` command-line option are **ignored for self-contained applications**.  
+> These options only apply to **framework-dependent applications**.  
+> For more information, see [Framework-dependent vs self-contained deployments](../deploying/index.md).
 
 The `APP_PATHS` property is not populated by default and is omitted for most applications.
 

docs/core/tools/dotnet-run.md

@@ -18,7 +18,7 @@
   [-a|--arch <ARCHITECTURE>] [--artifacts-path <ARTIFACTS_DIR>]
   [-c|--configuration <CONFIGURATION>] [-e|--environment <KEY=VALUE>]
   [--file <FILE_PATH>] [-f|--framework <FRAMEWORK>] [--force] [--interactive]
-  [--launch-profile <NAME>] [--no-build] [--no-dependencies]
+  [-lp|--launch-profile <NAME>] [--no-build] [--no-dependencies]
   [--no-launch-profile] [--no-restore] [--os <OS>] [--project <PATH>]
   [-r|--runtime <RUNTIME_IDENTIFIER>] [--tl:[auto|on|off]]
   [-v|--verbosity <LEVEL>] [[--] [application arguments]]
@@ -111,9 +111,11 @@
 
 - [!INCLUDE [interactive](../../../includes/cli-interactive.md)]
 
-- **`--launch-profile <NAME>`**
+- **``-lp|--launch-profile <NAME>``**
 
-  The name of the launch profile (if any) to use when launching the application. Launch profiles are defined in the *launchSettings.json* file and are typically called `Development`, `Staging`, and `Production`. For more information, see [Working with multiple environments](/aspnet/core/fundamentals/environments).
+  Specifies the name of the launch profile (if any) to use when launching the application.  
+  Launch profiles are defined in the *launchSettings.json* file and are typically named `Development`, `Staging`, or `Production`.  
+  For more information, see [Working with multiple environments](/aspnet/core/fundamentals/environments).
 
 - **`--no-build`**
 
@@ -236,4 +238,4 @@
   ```dotnetcli
   dotnet run -f net6.0 -arg1 -- arg2 arg3
   ```
-
+

docs/machine-learning/how-to-guides/install-gpu-model-builder.md

@@ -1,7 +1,7 @@
 ---
 title: How to install GPU support in Model Builder
 description: Learn how to install GPU support in Model Builder
-ms.date: 02/28/2023
+ms.date: 11/25/2025
 author: luisquintanilla
 ms.author: luquinta
 ms.topic: how-to
@@ -22,52 +22,69 @@ Learn how to install the GPU drivers to use your GPU with Model Builder.
 - [Model Builder Visual Studio extension](install-model-builder.md). The extension is built into Visual Studio as of version 16.6.1.
 - Make sure the appropriate [driver](https://www.nvidia.com/drivers) is installed for the GPU.
 
-### Image classification only
+### Image classification and object detection
 
 - NVIDIA developer account. If you don't have one, [create a free account](https://developer.nvidia.com/developer-program).
 - Install dependencies:
-  - Install [CUDA v10.1](https://developer.nvidia.com/cuda-10.1-download-archive-update2). Make sure you install CUDA v10.1, not any other newer version.
-  - Install [cuDNN v7.6.4 for CUDA 10.1](https://developer.nvidia.com/rdp/cudnn-archive) from the cuDNN archive. You cannot have multiple versions of cuDNN installed. After downloading the cuDNN v7.6.4 zip file and unpacking it, copy *\<CUDNN_zip_files_path>\cuda\bin\cudnn64_7.dll* to *\<YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin*.
+
+  - Install [CUDA **v11.8 or later**](https://developer.nvidia.com/cuda-downloads).
+
+    > [!NOTE]
+    > Using older CUDA versions such as 10.1 may cause *"no kernel found"* or similar errors when running object detection models in Model Builder.  
+    > CUDA 11.8 or newer provides better compatibility with the TensorFlow GPU runtime used by Model Builder.
+
+  - Install the corresponding **cuDNN** library version for your installed CUDA version from the [cuDNN archive](https://developer.nvidia.com/rdp/cudnn-archive).  
+    After downloading and unpacking the cuDNN zip file, copy  
+    `\<CUDNN_zip_files_path>\cuda\bin\cudnn64_*.dll`  
+    to  
+    `\<YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.x\bin`.
+
+    > [!TIP]
+    > You cannot have multiple versions of cuDNN installed simultaneously.  
+    > Ensure only one version matching your CUDA installation is present in your system path.
+
+---
 
 ## Troubleshooting
 
 **What if I don't have a GPU installed locally?**
 
-Deep learning scenarios tend to run faster on GPUs.
-
-Some scenarios like image classification support training on Azure GPU VMs.
+Deep learning scenarios tend to run faster on GPUs.  
+Some scenarios, such as image classification, support training on **Azure GPU VMs**.  
+However, if local GPUs or Azure are not an option, these scenarios can still run on CPU — though training times are significantly longer.
 
-However, if local GPUs or Azure are not an option for you, these scenarios also run on CPU. However, training times are significantly longer.  
+---
 
 **How do I know what GPU I have?**
 
 ***Check GPU from Settings***
 
-1. Right-click on the Windows start menu icon and select **Settings**.
-1. Select **Settings** > **System**
-1. Select **Display** and scroll down to **Related settings**.
-1. Select **Advanced display**. Your GPU's make and model are shown under **Display information**.
+1. Right-click the Windows start menu icon and select **Settings**.
+2. Select **System**.
+3. Choose **Display**, then scroll to **Related settings**.
+4. Select **Advanced display**. Your GPU's make and model appear under **Display information**.
 
 ***Check GPU from Task Manager***
 
-1. Right-click on the Windows start menu icon and select **Task Manager**.
-1. Select **Performance**.
-1. In the last pane of the tab, choose **GPU**. If this option is available, it will likely be at the bottom of the list.
-1. In the top right corner of the GPU selection, information about your computer's GPU is shown.
+1. Right-click the Windows start menu icon and select **Task Manager**.
+2. Go to the **Performance** tab.
+3. In the last pane, select **GPU** (usually at the bottom).
+4. The top-right corner displays information about your GPU.
 
-**I don't see my GPU in Settings or Task Manager but I know I have an NVIDIA GPU.**
+---
 
-1. Open Device Manager.
-1. Look at Display adapters.
-1. Install the appropriate [driver](https://www.nvidia.com/drivers) for your GPU.
+**I don't see my GPU in Settings or Task Manager, but I know I have an NVIDIA GPU.**
 
-**How do I see what version of CUDA I have?**
+1. Open **Device Manager**.
+2. Expand **Display adapters**.
+3. Install the appropriate [driver](https://www.nvidia.com/drivers) for your GPU.
 
-1. Open a PowerShell or command line window.
-1. Run the command `nvcc --version`.
+---
+
+**How do I see what version of CUDA I have?**
 
-**cuda is not available, please confirm you have a cuda-supported gpu**
+1. Open a PowerShell or Command Prompt window.
+2. Run the command:
 
-1. Open the [GeForce Experience](https://www.nvidia.com/en-us/geforce/geforce-experience/) app.
-1. The application should show installed and available driver updates. If you have trouble seeing updates, you can get the latest drivers from [https://www.nvidia.com/geforce/drivers/](https://www.nvidia.com/Download/index.aspx).
-1. Install the latest drivers.
+   ```bash
+   nvcc --version